{"ID":2883190,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08812","arxiv_id":"2508.08812","title":"TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models","abstract":"Personalized text-to-image generation aims to synthesize novel images of a specific subject or style using only a few reference images. Recent methods based on Low-Rank Adaptation (LoRA) enable efficient single-concept customization by injecting lightweight, concept-specific adapters into pre-trained diffusion models. However, combining multiple LoRA modules for multi-concept generation often leads to identity missing and visual feature leakage. In this work, we identify two key issues behind these failures: (1) token-wise interference among different LoRA modules, and (2) spatial misalignment between the attention map of a rare token and its corresponding concept-specific region. To address these issues, we propose Token-Aware LoRA (TARA), which introduces a token mask to explicitly constrain each module to focus on its associated rare token to avoid interference, and a training objective that encourages the spatial attention of a rare token to align with its concept region. Our method enables training-free multi-concept composition by directly injecting multiple independently trained TARA modules at inference time. Experimental results demonstrate that TARA enables efficient multi-concept inference and effectively preserving the visual identity of each concept by avoiding mutual interference between LoRA modules. The code and models are available at https://github.com/YuqiPeng77/TARA.","short_abstract":"Personalized text-to-image generation aims to synthesize novel images of a specific subject or style using only a few reference images. Recent methods based on Low-Rank Adaptation (LoRA) enable efficient single-concept customization by injecting lightweight, concept-specific adapters into pre-trained diffusion models....","url_abs":"https://arxiv.org/abs/2508.08812","url_pdf":"https://arxiv.org/pdf/2508.08812v1","authors":"[\"Yuqi Peng\",\"Lingtao Zheng\",\"Yufeng Yang\",\"Yi Huang\",\"Mingfu Yan\",\"Jianzhuang Liu\",\"Shifeng Chen\"]","published":"2025-08-12T10:14:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"LoRA\"]","has_code":false,"code_links":[{"ID":610962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2883190,"paper_url":"https://arxiv.org/abs/2508.08812","paper_title":"TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models","repo_url":"https://github.com/YuqiPeng77/TARA","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}